Sign Language Interpreter Aptitude
03 - BSL/SLI predictors analysis
Freya Watkins
University of Birmingham19 July 2022
Setup
Load packages
library(here) # file organisation & folder location
library(tidyverse) # data wrangling & plotting
library(scales) # scales on plots
library(patchwork) # for combining plots
library(lme4) # for linear mixed models
library(sjPlot) # for lmm outputR / package versions used
R.Version() ## $platform
## [1] "x86_64-w64-mingw32"
##
## $arch
## [1] "x86_64"
##
## $os
## [1] "mingw32"
##
## $system
## [1] "x86_64, mingw32"
##
## $status
## [1] ""
##
## $major
## [1] "4"
##
## $minor
## [1] "0.5"
##
## $year
## [1] "2021"
##
## $month
## [1] "03"
##
## $day
## [1] "31"
##
## $`svn rev`
## [1] "80133"
##
## $language
## [1] "R"
##
## $version.string
## [1] "R version 4.0.5 (2021-03-31)"
##
## $nickname
## [1] "Shake and Throw"packageVersion('here')## [1] '1.0.1'packageVersion('tidyverse')## [1] '1.3.1'packageVersion('scales') ## [1] '1.2.0'packageVersion('patchwork')## [1] '1.1.1'packageVersion('lme4') ## [1] '1.1.27.1'packageVersion('sjPlot') ## [1] '2.8.10'Read pre-wrangled data
(see 00-wrangling-setup.Rmd script)
# here::here()
apt <- readRDS(here("data", "apt-data.rds"))
apt <- as_tibble(apt)
head(apt)## # A tibble: 6 x 46
## session group ppt selection selected continuation continue L1_bsl
## <fct> <fct> <fct> <fct> <dbl> <fct> <dbl> <fct>
## 1 pre-degree first S70 selected 1 withdrew 1 0
## 2 pre-degree first S41 selected 1 withdrew 0 0
## 3 pre-degree first S63 selected 1 withdrew 0 0
## 4 pre-degree first S90 selected 1 withdrew 0 0
## 5 pre-degree first S60 selected 1 withdrew 0 0
## 6 pre-degree first S96 selected 1 continuing 1 1
## # ... with 38 more variables: self_rating <dbl>, bsl_years <dbl>,
## # nback_lett <dbl>, nback_spat <dbl>, nback_comb <dbl>, corsi_bspan <dbl>,
## # corsi_score <dbl>, corsi_corr <dbl>, corsi_mspan <dbl>, kirk_ceil <dbl>,
## # kirk_raw <dbl>, kirk_acc <dbl>, kbit_ceil <dbl>, kbit_raw <dbl>,
## # kbit_acc <dbl>, dspan_mem <dbl>, dspan_corr <dbl>, dspan_time <dbl>,
## # mr2d_acc <dbl>, mr2d_rt <dbl>, mr2d_sats <dbl>, mr3d_acc <dbl>,
## # mr3d_rt <dbl>, mr3d_sats <dbl>, bis_tot <dbl>, bis_att <dbl>, ...Filter out participants who did not progress beyond interview
apt <- apt %>% filter(selection == "selected")Filter out participants with deaf family members / L1 BSL
apt <- apt %>% filter(L1_bsl != 1)Convert data from long format to wide format
apt_wide <- apt %>%
tidyr::pivot_wider(names_from = session,
values_from = c(nback_lett:grade_terp))Plots & correlations
# set up a theme for the plots
theme_details <- theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size = 0.5),
plot.title = element_text(size = 15, face = "bold"),
plot.title.position = "plot",
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 14, colour = "black"),
axis.text.y = element_text(size = 14, colour = "black"),
legend.position = "none")BSL: predicted impact
First we look at the predictor variables which we hypothesised may have a relationship with BSL outcomes.
Corsi Blocks
vs BSL Grades
# Initial visuospatial skill vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Initial visuospatial skill vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial Corsi Blocks score")
a <- apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA")
# correlation
a %>% summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0000657# Initial visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Initial visuospatial skill vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial Corsi Blocks score")
#correlation
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0000256# 1st year visuospatial skill vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "1st year visuospatial skill vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "1st year Corsi Blocks score ")
# correlation
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.153# 1st year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "1st year visuospatial skill vs. 2nd year BSL grades",
y = "1st year BSL grade",
x = "2nd year Corsi Blocks score ")
# correlation
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0867# 2nd year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(7, 10)) +
theme_minimal() + theme_details +
labs(title = "2nd year visuospatial skill vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "2nd year Corsi Blocks score ")
# correlation
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00871vs BSL-SRT
Does Corsi Blocks performance relate to BSL Sentence Reproduction Task?
# Initial Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
coord_cartesian(xlim = c(6, 10)) +
theme_minimal() + theme_details +
labs(title = "Initial Corsi Blocks score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "Initial Corsi Blocks score")
# correlation
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.101# 2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores
( fig4a <- apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "a) 2nd year Visuospatial WM vs. 3rd year BSL-SRT score",
y = "3rd year BSL-SRT score",
x = "2nd year Corsi Blocks score") )
# correlation
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.609# linear model
lm(`bsl_srt_3rd year` ~ `corsi_corr_2nd year` + self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `corsi_corr_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## 0.50226 -1.33521 -1.85779 -3.44244 -0.01693 3.98307 2.39842 -0.23138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.3634 7.0008 -0.909 0.4051
## `corsi_corr_2nd year` 2.1591 0.7907 2.731 0.0412 *
## self_rating 0.1072 0.9523 0.113 0.9147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.793 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.6099, Adjusted R-squared: 0.4539
## F-statistic: 3.909 on 2 and 5 DF, p-value: 0.09503# 3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores
( fig4b <- apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "b) 3rd year Visuospatial WM vs. 3rd year BSL-SRT score",
y = "3rd year BSL-SRT score",
x = "3rd year Corsi Blocks score") )
# correlation
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.301# linear model
lm(`bsl_srt_3rd year` ~ `corsi_corr_3rd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `corsi_corr_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2436 -2.3397 0.2179 1.3910 5.9103
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9487 9.3428 -0.209 0.842
## `corsi_corr_3rd year` 1.6346 1.0570 1.547 0.173
## self_rating -0.1538 1.1578 -0.133 0.899
##
## Residual standard error: 3.409 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.3029, Adjusted R-squared: 0.07051
## F-statistic: 1.303 on 2 and 6 DF, p-value: 0.3388Combine plots for Figure 4 using patchwork:
( fig4 <- (fig4a | fig4b) )
ggsave("plots/fig4.jpeg", plot=fig4, width = 12, height = 5, units = "in")2D Mental Rotation
vs BSL Grades
Does 2D Mental Rotation performance relate to BSL grades?
# Initial 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "Initial 2D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial 2D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0939# Initial 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Initial 2D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial 2D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00345# 1st year 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "1st year 2D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "1st year 2D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr2d_sats_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.482# linear model
lm(`grade_bsl_1st year` ~ `mr2d_sats_1st year` + self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mr2d_sats_1st year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1327 -1.3366 0.5675 1.4788 5.5454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.9338 6.1350 11.399 2.73e-05 ***
## `mr2d_sats_1st year` -5.3368 3.0141 -1.771 0.127
## self_rating -0.3072 1.8532 -0.166 0.874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.991 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.4848, Adjusted R-squared: 0.3131
## F-statistic: 2.824 on 2 and 6 DF, p-value: 0.1367# interestingly, a negative correlation, but not significant
# 1st year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "1st year 2D Mental Rotation score vs. 2nd year BSL grades",
y = "1st year BSL grade",
x = "2nd year 2D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr2d_sats_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.121# 2nd year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
coord_cartesian(xlim = c(-1.3, 1.3)) +
theme_minimal() + theme_details +
labs(title = "2nd year 2D Mental Rotation vs. 2nd year BSL Grades",
y = "2nd year BSL Grades",
x = "2nd year 2D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.265# linear model
lm(`grade_bsl_2nd year` ~ `mr2d_sats_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mr2d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.458 -7.042 2.668 5.846 7.147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.653 5.782 10.316 1.74e-05 ***
## `mr2d_sats_2nd year` 4.438 2.702 1.642 0.144
## self_rating 1.660 2.466 0.673 0.522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.14 on 7 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.3094, Adjusted R-squared: 0.1121
## F-statistic: 1.568 on 2 and 7 DF, p-value: 0.2737vs BSL-SRT
Does 2D Mental Rotation performance relate to BSL Sentence Reproduction Task?
# 2nd year 2D Mental Rotation score vs. 2nd year BSL-SRT scores
# just 4 data points here
# 3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year 2D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.191# linear model
lm(`bsl_srt_3rd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3836 -0.7359 1.0129 1.5794 3.8603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.865 2.841 3.473 0.0133 *
## `mr2d_sats_3rd year` 3.108 2.074 1.499 0.1846
## self_rating 1.057 1.149 0.920 0.3932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.438 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2906, Adjusted R-squared: 0.05418
## F-statistic: 1.229 on 2 and 6 DF, p-value: 0.3573D Mental Rotation
vs BSL Grades
Does 3D Mental Rotation performance relate to BSL grades?
# Initial 3D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "Initial 3D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial 3D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0671# linear model
lm(`grade_bsl_1st year` ~ `mr3d_sats_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mr3d_sats_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.040 -5.622 -1.414 7.158 18.157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 65.4722 5.8011 11.286 2.56e-09 ***
## `mr3d_sats_pre-degree` 1.5614 1.3900 1.123 0.277
## self_rating -0.4048 2.1124 -0.192 0.850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.884 on 17 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.0691, Adjusted R-squared: -0.04042
## F-statistic: 0.6309 on 2 and 17 DF, p-value: 0.5441# Initial 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Initial 3D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial 3D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000282# 2nd year 3D Mental Rotation score vs. 2nd year BSL grades
( fig5b <- apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-1.2, 2.6)) +
theme_minimal() + theme_details +
labs(title = "b) 2nd year 3D Mental Rotation vs. 2nd year BSL Grade",
y = "2nd year BSL Grade",
x = "2nd year 3D Mental Rotation score") )
# correlation
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.386# linear model
lm(`grade_bsl_2nd year` ~ `mr3d_sats_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mr3d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.5850 -5.8497 0.7585 3.7502 9.7940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.8296 4.9508 12.489 5.48e-07 ***
## `mr3d_sats_2nd year` 4.6447 2.0988 2.213 0.0542 .
## self_rating -0.7094 2.3738 -0.299 0.7719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.986 on 9 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.3924, Adjusted R-squared: 0.2574
## F-statistic: 2.906 on 2 and 9 DF, p-value: 0.1062vs BSL-SRT
Does 3D Mental Rotation performance relate to BSL Sentence Reproduction Task?
# Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores
( fig5a <- apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-2.7, 1.2)) +
theme_minimal() + theme_details +
labs(title = "a) Initial 3D Mental Rotation vs. 3rd year BSL-SRT",
y = "3rd year BSL-SRT score",
x = "Initial 3D Mental Rotation score") )
# correlation
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.369lm(`bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8469 -0.7479 -0.2005 1.9028 3.2185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.55018 2.71305 5.363 0.00172 **
## `mr3d_sats_pre-degree` 1.90099 1.05072 1.809 0.12040
## self_rating -0.02618 1.06583 -0.025 0.98120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.243 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.3692, Adjusted R-squared: 0.1589
## F-statistic: 1.756 on 2 and 6 DF, p-value: 0.2511# 2nd year 3D Mental Rotation score vs. 2nd year BSL-SRT scores
# only 4 data points here
# 3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year 3D Mental Rotation score")
# correlation
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.123Combine plots for Figure 5 using patchwork:
( fig5 <- (fig5a | fig5b) )
ggsave("plots/fig5.jpeg", plot=fig5, width = 12, height = 5, units = "in")MLAT Number Learning
vs BSL Grades
# Initial MLAT Number Learning Accuracy vs. BSL grades 1st year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`grade_bsl_1st year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 1st year",
x = "MLAT Number Learning Accuracy", y = "BSL grade 1st year")
# correlation
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0215# linear model
lm(`grade_bsl_1st year` ~ `mlat_acc_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mlat_acc_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.9838 -7.0550 -0.3413 7.5274 14.1245
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.7371 13.3127 4.112 0.000728 ***
## `mlat_acc_pre-degree` 8.4969 12.4720 0.681 0.504872
## self_rating 0.7031 2.3654 0.297 0.769878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.11 on 17 degrees of freedom
## (10 observations deleted due to missingness)
## Multiple R-squared: 0.02658, Adjusted R-squared: -0.08794
## F-statistic: 0.2321 on 2 and 17 DF, p-value: 0.7954# Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`grade_bsl_2nd year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year",
x = "MLAT Number Learning Accuracy", y = "BSL grade 2nd year")
# correlation
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0333# linear model
lm(`grade_bsl_2nd year` ~ `mlat_acc_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mlat_acc_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.216 -7.027 3.224 7.449 17.760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.048 16.575 2.959 0.0104 *
## `mlat_acc_pre-degree` 13.098 15.646 0.837 0.4166
## self_rating 1.405 2.991 0.470 0.6459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.33 on 14 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.04827, Adjusted R-squared: -0.08769
## F-statistic: 0.355 on 2 and 14 DF, p-value: 0.7073vs BSL-SRT
# Initial MLAT Number Learning Accuracy vs. BSL-SRT 3rd year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`bsl_srt_3rd year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL-SRT score 3rd year",
x = "MLAT Number Learning Accuracy")
# correlation
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000354BSL: no predicted impact
Now we look at the predictor variables which we hypothesised were unlikely to have a relationship with BSL outcomes.
Kirklees Sentence Reading
vs BSL Grades
# Kirklees Sentence Reading pre-degree vs. BSL grades 1st year
( fig6a <- apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
coord_cartesian(xlim = c(.49, .96)) +
theme_minimal() + theme_details +
labs(title = "a) Initial English vocab vs. 1st year BSL Grade",
y = "1st year BSL Grade", x = "Initial Kirklees accuracy") )
# correlation
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.188# linear model
kirk0_bsl1 <- lm(`grade_bsl_1st year` ~ `kirk_acc_pre-degree` +
self_rating# + age_s1 # removing age for public version of code
, data = apt_wide)
summary(kirk0_bsl1)##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `kirk_acc_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.5382 -4.7901 0.6237 3.9859 13.0213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.187 11.757 2.738 0.0110 *
## `kirk_acc_pre-degree` 35.345 13.333 2.651 0.0135 *
## self_rating 1.318 1.356 0.972 0.3402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.713 on 26 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2161, Adjusted R-squared: 0.1558
## F-statistic: 3.583 on 2 and 26 DF, p-value: 0.04223tab_model(kirk0_bsl1, show.aic = T)| Â | grade_bsl_1st year | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 32.19 | 8.02 – 56.35 | 0.011 |
| kirk_acc_pre-degree | 35.35 | 7.94 – 62.75 | 0.013 |
| self_rating | 1.32 | -1.47 – 4.11 | 0.340 |
| Observations | 29 | ||
| R2 / R2 adjusted | 0.216 / 0.156 | ||
| AIC | 205.624 | ||
# Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.55, .95)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading pre-degree")
# correlation
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0189# Kirklees Sentence Reading 1st year vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 1st year",
y = "1st year BSL grade", x = "Kirklees Sentence Reading after 1 year")
# correlation
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0417# Kirklees Sentence Reading 1st year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading after 1 year")
# correlation
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0853# Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.6, 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading 2nd year")
# correlation
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00824vs BSL-SRT
Does Kirklees Sentence Reading performance relate to BSL Sentence Reproduction Task?
# Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
( fig6b <- apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2, position="jitter", seed = 42) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
coord_cartesian(xlim = c(.56, .93)) +
theme_minimal() + theme_details +
labs(title = "b) Initial English vocab vs. 3rd year BSL-SRT score",
y = "3rd year BSL-SRT score", x = "Initial Kirklees accuracy") )
# correlation
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.237lm(`bsl_srt_3rd year` ~ `kirk_acc_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `kirk_acc_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1089 -1.8031 -0.1845 2.5358 4.6470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1142 9.3522 -0.226 0.829
## `kirk_acc_pre-degree` 16.9416 10.8402 1.563 0.169
## self_rating 0.8558 1.0985 0.779 0.466
##
## Residual standard error: 3.398 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.3071, Adjusted R-squared: 0.0761
## F-statistic: 1.329 on 2 and 6 DF, p-value: 0.3327# 2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year Kirklees Sentence Reading score")
# correlation
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.149Combine plots for Figure 6 using patchwork:
( fig6 <- (fig6a | fig6b) )
ggsave("plots/fig6.jpeg", plot=fig6, width = 12, height = 5, units = "in")Summarising Task
vs BSL Grades
# Summarising pre-degree vs. BSL Grades 1st year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `grade_bsl_1st year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. BSL Grades 1st year",
y = "1st year BSL grade", x = "Summarising pre-degree")
# correlation
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00209# Summarising pre-degree vs. BSL Grades 2nd year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. BSL Grades 2nd year",
y = "2nd year BSL grade", x = "Summarising pre-degree")
# correlation
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0588vs BSL-SRT
# Summarising pre-degree vs. BSL-SRT 3rd year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. BSL-SRT 3rd year",
y = "BSL-SRT 3rd year", x = "Summarising pre-degree")
# correlation
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0108KBIT-2 Matrices
vs BSL Grades
# KBIT-2 Matrices pre-degree vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 1st year",
y = "1st year BSL grade", x = "KBIT-2 Matrices pre-degree")
# correlation
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0115# KBIT-2 Matrices pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices pre-degree")
# correlation
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0134# KBIT-2 Matrices 1st year vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 1st year",
y = "1st year BSL grade", x = "KBIT-2 Matrices 1st year")
# correlation
apt_wide %>%
filter(`kbit_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0273# KBIT-2 Matrices 1st year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices 1st year")
# correlation
apt_wide %>%
filter(`kbit_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0000593# KBIT-2 Matrices 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 2nd year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices 2nd year")
# correlation
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.167vs BSL-SRT
Does KBIT-2 Matrices performance relate to BSL Sentence Reproduction Task?
# Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.84, 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "Initial KBIT-2 Matrices score")
# correlation
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00494# 2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year KBIT-2 Matrices score")
# correlation
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0933Dual N-Back
vs BSL Grades
# Dual N-Back pre-degree vs. BSL Grades 1st year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. BSL Grades 1st year",
y = "1st year BSL grade", x = "Dual N-Back pre-degree")
# correlation
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0190# Dual N-Back pre-degree vs. BSL Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. BSL Grades 2nd year",
y = "2nd year BSL grade", x = "Dual N-Back pre-degree")
# correlation
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0101# Dual N-Back 2nd year vs. BSL Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 2nd year vs. BSL Grades 2nd year",
y = "2nd year BSL grade", x = "Dual N-Back 2nd year")
# correlation
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0520vs BSL-SRT
Does Dual N-Back performance relate to BSL Sentence Reproduction Task?
# Initial Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.71, .75)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Initial Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "Initial Dual N-Back score")
# correlation
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00186# 2nd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "2nd year Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "2nd year Dual N-Back score")
# correlation
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.218# 3rd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "3rd year Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year Dual N-Back score")
# correlation
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.122# linear model
lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3928 -0.6371 -0.3928 0.6072 6.9485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -19.9281 38.7294 -0.515 0.625
## `nback_comb_3rd year` 46.3473 56.0801 0.826 0.440
## self_rating -0.1952 1.4719 -0.133 0.899
##
## Residual standard error: 3.82 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.1246, Adjusted R-squared: -0.1671
## F-statistic: 0.4272 on 2 and 6 DF, p-value: 0.6707Digit Span
vs BSL Grades
# Digit Span pre-degree vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. BSL grades 1st year",
y = "1st year BSL grade", x = "Digit Span pre-degree")
# correlation
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.122# Digit Span pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Digit Span pre-degree")
# correlation
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.141# No datapoints for Digit Span @ '1 year of study'
# Digit Span 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 2nd year vs BSL grades 2nd year",
y = "2nd year BSL grade", x = "Digit Span 2nd year")
# correlation
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0796vs BSL-SRT
Does Digit Span score relate to BSL-SRT score?
# Initial Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Initial Digit Span score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "Initial Digit Span score")
# correlation
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00124# 2nd year Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "2nd year Digit Span score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year Digit Span score")
# correlation
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00433Barratt Impulsiveness Scale
vs BSL Grades
# Barratt Impulsiveness Scale 2nd year vs BSL grades 1st year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_bsl_1st year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity vs. BSL grades 1st year",
y = "1st year BSL grade", x = "BIS 2nd year")
# correlation
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0468# Barratt Impulsiveness Scale 2nd year vs BSL grades 2nd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_bsl_2nd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "BIS 2nd year")
# correlation
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00858# Barratt Impulsiveness Scale 2nd year vs BSL self-rated proficiency
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(self_rating != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = self_rating)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity vs. Initial BSL self-rating",
y = "Initial BSL self-rating", x = "BIS 2nd year")
vs BSL-SRT
# Barratt Impulsiveness Scale 2nd year vs BSL-SRT 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`bsl_srt_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity 2nd year vs. BSL-SRT 3rd year",
y = "3rd year BSL-SRT score", x = "BIS Score 2nd year")
# correlation
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.272# linear model
lm(`bsl_srt_3rd year` ~ `bis_tot_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `bis_tot_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7743 -1.2297 0.4997 2.5887 4.0756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.0529 9.3110 2.691 0.036 *
## `bis_tot_2nd year` -0.1815 0.1248 -1.454 0.196
## self_rating -0.2987 1.2260 -0.244 0.816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.466 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2791, Adjusted R-squared: 0.03883
## F-statistic: 1.162 on 2 and 6 DF, p-value: 0.3746Interpreting: predicted impact
Now we turn to look at the predictor variables which we hypothesised may have a relationship with SLI outcomes.
Dual N-Back
vs Interpreting Grades
# Dual N-Back pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.71, .75)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Dual N-Back pre-degree")
# correlation
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0123# Dual N-Back pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Dual N-Back pre-degree")
# correlation
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0992# Dual N-Back 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Dual N-Back 2nd year")
# correlation
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000705vs Eng>BSL interpreting
# Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.71, .75)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back pre-degree")
# correlation
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.125# Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 2nd year")
# correlation
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.121# Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 3rd year")
# correlation
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.155# linear model
lm(`terp_e2b_3rd year` ~ `nback_comb_3rd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.3649 -5.7844 -1.9770 0.6793 20.5151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -38.624 133.735 -0.289 0.782
## `nback_comb_3rd year` 136.419 193.649 0.704 0.508
## self_rating 1.444 5.083 0.284 0.786
##
## Residual standard error: 13.19 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.1663, Adjusted R-squared: -0.1117
## F-statistic: 0.5982 on 2 and 6 DF, p-value: 0.5796vs BSL>Eng interpreting
# Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.71, .75)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back pre-degree")
# correlation
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0605# Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 2nd year")
# correlation
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.178# Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 3rd year")
# correlation
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.103Corsi Blocks
vs Interpreting Grades
# Corsi Blocks pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(8, 10)) +
theme_minimal() + theme_details +
labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Corsi Blocks pre-degree")
# correlation
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0114# Corsi Blocks pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks pre-degree")
# correlation
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00378# Corsi Blocks 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Corsi Blocks 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks 1st year")
# correlation
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.164# Corsi Blocks 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(7, 10)) +
theme_minimal() + theme_details +
labs(title = "Corsi Blocks 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks 2nd year")
# correlation
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0791vs Eng>BSL interpreting
# Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(6, 10)) +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks pre-degree")
# correlation
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.289# linear model
lm(`terp_e2b_3rd year` ~ `corsi_corr_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `corsi_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.9254 -8.4322 -0.1853 1.9101 22.9235
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.58284 29.41334 0.666 0.53
## `corsi_corr_pre-degree` 5.07783 3.98831 1.273 0.25
## self_rating -0.06448 4.75634 -0.014 0.99
##
## Residual standard error: 12.18 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2893, Adjusted R-squared: 0.0524
## F-statistic: 1.221 on 2 and 6 DF, p-value: 0.359# Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year
( fig7a <- apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(7, 11)) +
theme_minimal() + theme_details +
labs(title = "a) 2nd year Visuospatial WM vs. 3rd yr Eng>BSL interp.",
y = "3rd year Eng>BSL interp. score",
x = "2nd year Corsi Blocks score") )
# correlation
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.565# linear model
lm(`terp_e2b_3rd year` ~ `corsi_corr_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `corsi_corr_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## -8.536 -1.520 2.139 -6.065 -9.681 3.519 8.615 11.529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3786 23.2375 0.059 0.9550
## `corsi_corr_2nd year` 6.5196 2.6245 2.484 0.0556 .
## self_rating 0.6451 3.1611 0.204 0.8463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.271 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.5687, Adjusted R-squared: 0.3962
## F-statistic: 3.297 on 2 and 5 DF, p-value: 0.1222# Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks 3rd yr vs. Eng to BSL interpreting 3rd yr",
y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 3rd year")
# correlation
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.132vs BSL>Eng interpreting
# Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(6, 10)) +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks pre-degree")
# correlation
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0578# Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 2nd year")
# correlation
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.455# linear model
lm(`terp_b2e_3rd year` ~ `corsi_corr_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `corsi_corr_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22
## -0.3476 -0.3357 4.8847 -2.7752 -3.9221 -1.3221 3.8180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.2283 10.4934 4.310 0.0125 *
## `corsi_corr_2nd year` 2.2068 1.2131 1.819 0.1430
## self_rating -0.3604 1.5418 -0.234 0.8266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.984 on 4 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.4624, Adjusted R-squared: 0.1937
## F-statistic: 1.721 on 2 and 4 DF, p-value: 0.289# Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 3rd year")
# correlation
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0169Digit Span
vs Interpreting Grades
# Digit Span pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.6, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Digit Span pre-degree")
# correlation
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0155# Digit Span pre-degree vs. Interpreting Grades 2nd year
( fig7b <- apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
coord_cartesian(xlim = c(.51, .87)) +
theme_minimal() + theme_details +
labs(title = "b) Initial Auditory WM vs. 2nd year Interpreting Grade",
y = "2nd year Interpreting Grade",
x = "Initial Digit Span Accuracy") )
# correlation
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.338# linear model
lm(`grade_terp_2nd year` ~ `dspan_corr_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `dspan_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.043 -3.731 2.012 5.848 8.327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.232 18.749 0.706 0.4965
## `dspan_corr_pre-degree` 58.906 23.551 2.501 0.0314 *
## self_rating 2.033 2.088 0.973 0.3534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.528 on 10 degrees of freedom
## (17 observations deleted due to missingness)
## Multiple R-squared: 0.3954, Adjusted R-squared: 0.2745
## F-statistic: 3.27 on 2 and 10 DF, p-value: 0.08079# Digit Span 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Digit Span pre-degree")
# correlation
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0709vs Eng>BSL interpreting
# Digit Span pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span pre-degree")
# correlation
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0926# Digit Span 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span 2nd year")
# correlation
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0851# Digit Span 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.7, .86)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span 3rd year")
# correlation
apt_wide %>%
filter(`dspan_corr_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.115vs BSL>Eng interpreting
# Digit Span pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .72)) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span pre-degree")
# correlation
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00697# Digit Span 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .79)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span 2nd year")
# correlation
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0863# Digit Span 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Digit Span 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span 3rd year")
# correlation
apt_wide %>%
filter(`dspan_corr_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0264Combine plots for Figure 7 using patchwork:
( fig7 <- (fig7a | fig7b) )
ggsave("plots/fig7.jpeg", plot=fig7, width = 12, height = 5, units = "in")Summarising Task
vs Interpreting Grades
# Summarising pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Summarising pre-degree")
# correlation
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0334vs Eng>BSL interpreting
# Summarising pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. English to BSL interpreting 3rd year",
y = "English to BSL interpreting 3rd year", x = "Summarising pre-degree")
# correlation
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0447vs BSL>Eng interpreting
# Summarising pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. BSL to English interpreting 3rd year",
y = "BSL to English interpreting 3rd year", x = "Summarising pre-degree")
# correlation
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00303Interpreting: no predicted impact
Now we examine the predictor variables where we hypothesised there was unlikely to be a relationship with SLI outcomes.
Kirklees Sentence Reading
vs Interpreting Grades
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.48, .93)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")
# correlation
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.160# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.56, .93)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")
# correlation
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0883# Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading after 1 year")
# correlation
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0201# Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading 2nd year")
# correlation
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0457vs Eng>BSL interpreting
# Kirklees pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.56, .93)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Kirklees pre-degree")
# correlation
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0754# Kirklees 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Kirklees 2nd year")
# correlation
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0193vs BSL>Eng interpreting
# Kirklees pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.56, .93)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Kirklees pre-degree")
# correlation
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0922# Kirklees 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Kirklees 2nd year")
# correlation
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000130KBIT-2 Matrices
vs Interpreting Grades
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.82, .95)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "KBIT-2 Matrices pre-degree")
# correlation
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0419# KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices pre-degree")
# correlation
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0232# KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 1st year")
# correlation
apt_wide %>%
filter(`kbit_acc_1st year` != "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00117# KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 2nd year")
# correlation
apt_wide %>%
filter(`kbit_acc_2nd year` != "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.116vs Eng>BSL interpreting
# KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.84, 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")
# correlation
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000458# KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")
# correlation
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000597vs BSL>Eng interpreting
# KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.84, 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")
# correlation
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0417# KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")
# correlation
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.01722D Mental Rotation
vs Interpreting Grades
# 2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "2D Mental Rotation pre-degree")
# correlation
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0129# 2D Mental Rotation 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-1.5, 1.5)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "2D Mental Rotation 1st year")
# correlation
apt_wide %>%
filter(`mr2d_sats_2nd year` != "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.150lm(`grade_terp_2nd year` ~ `mr2d_sats_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr2d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.991 -5.482 -2.225 7.289 12.136
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 56.268 7.004 8.033 8.88e-05 ***
## `mr2d_sats_2nd year` 3.728 3.273 1.139 0.292
## self_rating 1.610 2.987 0.539 0.607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.86 on 7 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1841, Adjusted R-squared: -0.04897
## F-statistic: 0.7899 on 2 and 7 DF, p-value: 0.4905# 2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-.5, 1)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 3rd year vs. Interpreting Grades 2nd year",
y = "3rd year Interpreting grade", x = "2D Mental Rotation 2nd year")
# correlation
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.179# linear model
lm(`grade_terp_2nd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 26 28
## 5.237 1.013 -6.849 -1.215 1.955 7.107 -11.393 4.146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.522 6.334 7.661 0.000604 ***
## `mr2d_sats_3rd year` 10.531 5.893 1.787 0.133989
## self_rating 3.986 2.584 1.543 0.183573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.458 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.4436, Adjusted R-squared: 0.221
## F-statistic: 1.993 on 2 and 5 DF, p-value: 0.2309vs Eng>BSL interpreting
# 2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 2nd year")
# correlation
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.198# linear model
lm(`terp_e2b_3rd year` ~ `mr2d_sats_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr2d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## -12.4203 3.6479 -13.5813 8.6741 -5.9900 -0.5966 11.9458 8.3205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.374 8.536 6.370 0.00141 **
## `mr2d_sats_2nd year` 6.633 4.569 1.452 0.20624
## self_rating 3.948 4.121 0.958 0.38199
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.62 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.3222, Adjusted R-squared: 0.05108
## F-statistic: 1.188 on 2 and 5 DF, p-value: 0.3782# 2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 3rd year")
# correlation
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.180# linear model
lm(`terp_e2b_3rd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.6489 -6.8092 0.7963 8.7048 10.6778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.221 9.204 5.131 0.00216 **
## `mr2d_sats_3rd year` 11.842 6.718 1.763 0.12840
## self_rating 5.611 3.722 1.507 0.18245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.14 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.4053, Adjusted R-squared: 0.2071
## F-statistic: 2.045 on 2 and 6 DF, p-value: 0.2103vs BSL>Eng interpreting
# 2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
( fig8a <- apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
coord_cartesian(xlim = c(-1.3, 1.3)) +
theme_minimal() + theme_details +
labs(title = "a) 2nd year 2D Mental Rotation vs. 3rd year BSL>Eng interp.",
y = "3rd year BSL>Eng interpreting", x = "2nd year 2D Mental Rotation") )
# correlation
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.602# linear model
lm(`terp_b2e_3rd year` ~ `mr2d_sats_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr2d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22
## -2.1612 0.6959 -1.4352 3.7724 -0.9446 -2.6385 2.7113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.648 2.243 27.933 9.77e-06 ***
## `mr2d_sats_2nd year` 3.648 1.239 2.944 0.0422 *
## self_rating 1.205 1.134 1.062 0.3479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.027 on 4 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.6898, Adjusted R-squared: 0.5346
## F-statistic: 4.446 on 2 and 4 DF, p-value: 0.09625# 2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
( fig8b <- apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
coord_cartesian(xlim = c(-0.5, 1.4)) +
theme_minimal() + theme_details +
labs(title = "b) 3rd yr 2D Mental Rotation vs. 3rd yr BSL>Eng interp.",
y = "3rd year BSL>Eng interpreting", x = "3rd year 2D Mental Rotation") )
# correlation
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.311# linear model
lm(`terp_b2e_3rd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 28
## -3.0760 2.8403 -0.2463 2.5299 -1.6912 -5.1393 2.8670 1.9156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60.032 3.008 19.955 5.84e-06 ***
## `mr2d_sats_3rd year` 4.599 2.177 2.112 0.0884 .
## self_rating 1.772 1.276 1.389 0.2235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.61 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.5025, Adjusted R-squared: 0.3035
## F-statistic: 2.525 on 2 and 5 DF, p-value: 0.17463D Mental Rotation
vs Interpreting Grades
# 3D Mental Rotation pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-2, 1.5)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "3D Mental Rotation pre-degree")
# correlation
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0677# 3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title= "3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation pre-degree") 
# correlation
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.182# 3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title= "3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation 2nd year") 
# correlation
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.261# 3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title= "3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation 3rd year") 
# correlation
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.435# linear model
lm(`grade_terp_2nd year` ~ `mr3d_sats_3rd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr3d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 26 28
## -1.4232 0.2050 -8.6905 0.3706 -2.2990 9.8876 -4.3629 6.3124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.449 5.537 8.931 0.000293 ***
## `mr3d_sats_3rd year` 5.877 2.768 2.123 0.087131 .
## self_rating 2.071 2.189 0.946 0.387719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.923 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.5206, Adjusted R-squared: 0.3288
## F-statistic: 2.714 on 2 and 5 DF, p-value: 0.1592vs Eng>BSL interpreting
# 3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year
( fig8c <- apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-2.7, 1.1)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "c) Initial 3D Mental Rotation vs. 3rd yr Eng>BSL interp.",
y = "3rd year Eng>BSL interpreting", x = "Initial 3D Mental Rotation") )
# correlation
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.268# linear model
lm(`terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.243 -5.679 1.404 2.243 18.096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.273 10.114 6.157 0.000842 ***
## `mr3d_sats_pre-degree` 5.158 3.917 1.317 0.235955
## self_rating 2.062 3.973 0.519 0.622317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.09 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2997, Adjusted R-squared: 0.06625
## F-statistic: 1.284 on 2 and 6 DF, p-value: 0.3435# 3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-1.2, 2.2)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 2nd year")
# correlation
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0974# 3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 3rd year")
# correlation
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00752vs BSL>Eng interpreting
# 3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year
( fig8d <- apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-2.7, 1.2)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "d) Initial 3D Mental Rotation vs. 3rd yr BSL>Eng interp.",
y = "3rd year BSL>Eng interpreting", x = "Initial 3D Mental Rotation") )
# correlation
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.475# linear model
lm(`terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 28
## -4.4451 0.3171 0.6291 -0.3183 -1.4933 -2.9145 2.7963 5.4287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.4684 3.1264 21.260 4.27e-06 ***
## `mr3d_sats_pre-degree` 2.4042 1.1997 2.004 0.101
## self_rating 0.2235 1.2903 0.173 0.869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.698 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.4779, Adjusted R-squared: 0.2691
## F-statistic: 2.289 on 2 and 5 DF, p-value: 0.1969# 3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-1.2, 2.2)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")
# correlation
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.286# 3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")
# correlation
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.158Combine plots for Figure 8 using patchwork:
( fig8 <- (fig8a | fig8b)/(fig8c | fig8d) )
ggsave("plots/Fig8.jpeg", plot=fig8, width = 12, height = 8, units = "in")Barratt Impulsiveness Scale
vs Interpreting Grades
# Barratt Impulsiveness Scale 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_terp_2nd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "BIS score 2nd year")
# correlation
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0000311vs Eng>BSL interpreting
# Barratt Impulsiveness Scale 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`terp_e2b_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity 2nd year vs. Eng to BSL interpreting 3rd year",
y = "2nd year Interpreting grade", x = "Impulsivity 2nd year")
# correlation
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.115vs BSL>Eng interpreting
# Barratt Impulsiveness Scale 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`terp_b2e_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity 2nd year vs. BSL to Eng interpreting 3rd year",
y = "2nd year Interpreting grade", x = "Impulsivity 2nd year")
# correlation
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.236Correlations among predictor variables
Here we look at the relationships among predictor variables to see if they correlate
# Do 2D- and 3D-MR correlate in second year?
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `mr3d_sats_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "2nd year 2D Mental Rotation vs. 2nd year 3D Mental Rotation",
y = "2nd year 2D Mental Rotation", x = "2nd year 3D Mental Rotation")
# correlation
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`mr3d_sats_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`mr3d_sats_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.304# linear model
lm(`mr2d_sats_2nd year` ~ `mr3d_sats_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `mr2d_sats_2nd year` ~ `mr3d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6104 -0.5109 0.1422 0.5531 1.0200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4178 0.6252 0.668 0.5228
## `mr3d_sats_2nd year` 0.6131 0.2556 2.399 0.0433 *
## self_rating -0.3866 0.3076 -1.257 0.2442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8792 on 8 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.4185, Adjusted R-squared: 0.2732
## F-statistic: 2.879 on 2 and 8 DF, p-value: 0.1143# Do 2D- and 3D-MR correlate in third year?
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `mr3d_sats_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "3rd year 2D Mental Rotation vs. 3rd year 3D Mental Rotation",
y = "3rd year 2D Mental Rotation", x = "3rd year 3D Mental Rotation")
# correlation
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`mr3d_sats_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`mr3d_sats_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.491# linear model
lm(`mr3d_sats_3rd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `mr3d_sats_3rd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0954 -0.2439 -0.1139 0.3497 1.1442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1346 0.6280 0.214 0.8374
## `mr2d_sats_3rd year` 1.2452 0.4584 2.717 0.0348 *
## self_rating 0.2280 0.2540 0.898 0.4039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.76 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.5516, Adjusted R-squared: 0.4021
## F-statistic: 3.69 on 2 and 6 DF, p-value: 0.09017# significantly correlate with each other
# Does initial copy-sign correlate with initial BSL self-ratings?
apt_wide %>%
ggplot(aes(x = `copy_sign_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Initial copy-sign score vs. 1st year BSL grade",
y = "Initial copy-sign score", x = "1st year BSL grade")
# correlation
apt_wide %>%
filter(`copy_sign_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`copy_sign_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.459# linear model
lm(`grade_bsl_1st year` ~ `copy_sign_pre-degree` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `copy_sign_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.3659 -1.3727 0.8047 3.6029 6.4859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.1538 17.2326 1.286 0.2346
## `copy_sign_pre-degree` 0.5650 0.2509 2.252 0.0544 .
## self_rating -0.6055 1.9452 -0.311 0.7635
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.641 on 8 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.4658, Adjusted R-squared: 0.3322
## F-statistic: 3.487 on 2 and 8 DF, p-value: 0.08147# marginally insignificantCorrelations among outcome variables
Now we look at relationships among outcome variables to see if they correlate
# Does interpreting Eng>BSL correlate with interpreting BSL>Eng?
apt_wide %>%
ggplot(aes(x = `terp_e2b_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "3rd year English-to-BSL interpreting vs. 3rd year BSL-to-English interpreting",
y = "3rd year BSL-to-English interpreting", x = "3rd year English-to-BSL interpreting")
# correlation
apt_wide %>%
filter(`terp_e2b_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`terp_e2b_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.788# linear model
lm(`terp_b2e_3rd year` ~ `terp_e2b_3rd year` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `terp_e2b_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 28
## 0.6655 0.4638 3.6894 -1.1593 -1.3687 -2.6088 1.4733 -1.1552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.06528 4.25053 11.073 0.000105 ***
## `terp_e2b_3rd year` 0.29092 0.07020 4.144 0.008959 **
## self_rating -0.04233 0.82549 -0.051 0.961090
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.358 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.7877, Adjusted R-squared: 0.7028
## F-statistic: 9.278 on 2 and 5 DF, p-value: 0.02076# significant predictor
# Does interpreting grades correlate with interpreting Eng>BSL?
apt_wide %>%
ggplot(aes(x = `grade_terp_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(50, 70)) +
theme_minimal() + theme_details +
labs(title= "2nd year interpreting grade vs. 3rd year English-to-BSL interpreting",
y = "3rd year English-to-BSL interpreting", x = "2nd year interpreting grade")
# correlation
apt_wide %>%
filter(`terp_e2b_3rd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`grade_terp_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0640# linear model
lm(`terp_e2b_3rd year` ~ `grade_terp_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `grade_terp_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 26 28
## -13.582 9.169 -5.995 3.637 -10.743 -1.039 5.549 13.005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.7561 29.8472 1.433 0.211
## `grade_terp_2nd year` 0.1840 0.5275 0.349 0.741
## self_rating 3.1036 3.7296 0.832 0.443
##
## Residual standard error: 11.26 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.1779, Adjusted R-squared: -0.1509
## F-statistic: 0.541 on 2 and 5 DF, p-value: 0.6128# Does interpreting grades correlate with interpreting BSL>Eng?
apt_wide %>%
ggplot(aes(x = `grade_terp_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(50, 70)) +
theme_minimal() + theme_details +
labs(title= "2nd year interpreting grade vs. 3rd year BSL-to-English interpreting",
y = "3rd year BSL-to-English interpreting", x = "2nd year interpreting grade")
# correlation
apt_wide %>%
filter(`terp_b2e_3rd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`grade_terp_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0426# linear model
lm(`terp_b2e_3rd year` ~ `grade_terp_2nd year` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `grade_terp_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 28
## -2.3229 3.3993 2.1240 0.1452 -4.3825 -2.3748 3.4118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60.68928 11.48602 5.284 0.00615 **
## `grade_terp_2nd year` 0.03117 0.20838 0.150 0.88833
## self_rating 0.70349 1.45773 0.483 0.65461
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.807 on 4 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.09532, Adjusted R-squared: -0.357
## F-statistic: 0.2107 on 2 and 4 DF, p-value: 0.8184# Do interpreting grades correlate with BSL grades?
apt_wide %>%
ggplot(aes(x = `grade_terp_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "2nd year interpreting grade vs. 2nd year BSL grade",
y = "2nd year BSL grade", x = "2nd year interpreting grade")
# correlation
apt_wide %>%
filter(`grade_bsl_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`grade_bsl_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.685# linear model
lm(`grade_bsl_2nd year` ~ `grade_terp_2nd year` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `grade_terp_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.5858 -2.1350 -0.2428 1.7075 10.6495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.3815 6.9110 2.226 0.0371 *
## `grade_terp_2nd year` 0.7753 0.1142 6.787 1.03e-06 ***
## self_rating 0.7872 0.9090 0.866 0.3963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.013 on 21 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.6963, Adjusted R-squared: 0.6673
## F-statistic: 24.07 on 2 and 21 DF, p-value: 3.682e-06# significantly correlate
# Do 1st year BSL grades correlate with 2nd year interpreting grades?
apt_wide %>%
ggplot(aes(x = `grade_bsl_1st year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(56, 79)) +
theme_minimal() + theme_details +
labs(title= "2nd year interpreting grade vs. 1st year BSL grade",
y = "2nd year interpreting grade", x = "1st year BSL grade")
# correlation
apt_wide %>%
filter(`grade_bsl_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`grade_bsl_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.637# linear model
lm(`grade_terp_2nd year` ~ `grade_bsl_1st year` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `grade_bsl_1st year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.805 -3.752 0.048 2.102 14.080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12.0606 11.0658 -1.090 0.288
## `grade_bsl_1st year` 1.1474 0.1784 6.433 2.24e-06 ***
## self_rating -1.4193 1.0551 -1.345 0.193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.556 on 21 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.6658, Adjusted R-squared: 0.6339
## F-statistic: 20.92 on 2 and 21 DF, p-value: 1.006e-05# significantly correlate
# Does initial copy-sign correlate with 1st-year BSL grades?
apt_wide %>%
ggplot(aes(x = `copy_sign_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Initial copy-sign score vs. 1st year BSL grade",
y = "1st year BSL grade", x = "Initial copy-sign score")
# correlation
apt_wide %>%
filter(`copy_sign_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`copy_sign_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.459# linear model
lm(`grade_bsl_1st year` ~ `copy_sign_pre-degree` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `copy_sign_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.3659 -1.3727 0.8047 3.6029 6.4859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.1538 17.2326 1.286 0.2346
## `copy_sign_pre-degree` 0.5650 0.2509 2.252 0.0544 .
## self_rating -0.6055 1.9452 -0.311 0.7635
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.641 on 8 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.4658, Adjusted R-squared: 0.3322
## F-statistic: 3.487 on 2 and 8 DF, p-value: 0.08147# interestingly, no relationship
# Does initial copy-sign correlate with 2nd-year BSL grades?
apt_wide %>%
ggplot(aes(x = `copy_sign_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Initial copy-sign score vs. 2nd year BSL grade",
y = "2nd year BSL grade", x = "Initial copy-sign score")
# correlation
apt_wide %>%
filter(`copy_sign_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`copy_sign_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.210# linear model
lm(`grade_bsl_2nd year` ~ `copy_sign_pre-degree` +
self_rating, data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `copy_sign_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.9987 -2.5943 -0.9078 5.5478 8.5903
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.97137 28.04795 1.140 0.298
## `copy_sign_pre-degree` 0.36337 0.43065 0.844 0.431
## self_rating 0.04689 3.18968 0.015 0.989
##
## Residual standard error: 7.48 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2099, Adjusted R-squared: -0.05349
## F-statistic: 0.7969 on 2 and 6 DF, p-value: 0.4933# no relationship
# Does initial copy-sign correlate with later terp grades?
apt_wide %>%
ggplot(aes(x = `copy_sign_pre-degree`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Initial copy-sign score vs. 2nd year interpreting grade",
y = "2nd year interpreting grade", x = "Initial copy-sign score")
# correlation
apt_wide %>%
filter(`copy_sign_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`copy_sign_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.217# linear model
lm(`grade_terp_2nd year` ~ `copy_sign_pre-degree` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `copy_sign_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.6507 -4.9029 -0.2995 5.9080 8.9164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.83191 27.37127 1.053 0.333
## `copy_sign_pre-degree` 0.36033 0.42026 0.857 0.424
## self_rating 0.06346 3.11272 0.020 0.984
##
## Residual standard error: 7.3 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2168, Adjusted R-squared: -0.04423
## F-statistic: 0.8306 on 2 and 6 DF, p-value: 0.4804# interestingly yes (marginal)
# does initial copy-sign correlate with 3rd year BSL-SRT?
apt_wide %>%
ggplot(aes(x = `copy_sign_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(70, 90)) +
theme_minimal() + theme_details +
labs(title = "Initial copy-sign score vs. 3rd year BSL-SRT score",
y = "3rd year BSL-SRT score", x = "Initial copy-sign score")
# correlation
apt_wide %>%
filter(`copy_sign_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`copy_sign_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.513# linear model
lm(`bsl_srt_3rd year` ~ `copy_sign_pre-degree` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `copy_sign_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 17 18 22 26 28
## 1.636 -2.239 2.735 3.379 -2.915 -2.596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -19.3673 17.5120 -1.106 0.349
## `copy_sign_pre-degree` 0.4203 0.2441 1.722 0.184
## self_rating -0.8146 1.9695 -0.414 0.707
##
## Residual standard error: 3.734 on 3 degrees of freedom
## (24 observations deleted due to missingness)
## Multiple R-squared: 0.5396, Adjusted R-squared: 0.2326
## F-statistic: 1.758 on 2 and 3 DF, p-value: 0.3124# do 1st year BSL grades correlate with 3rd year BSL-SRT?
apt_wide %>%
ggplot(aes(x = `grade_bsl_1st year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(56, 78)) +
theme_minimal() + theme_details +
labs(title = "1st year BSL grade vs. 3rd year BSL-SRT score",
y = "3rd year BSL-SRT score", x = "1st year BSL grade")
# correlation
apt_wide %>%
filter(`grade_bsl_1st year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`grade_bsl_1st year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.661# linear model
lm(`bsl_srt_3rd year` ~ `grade_bsl_1st year` +
self_rating, data = apt_wide) %>% summary() # significant predictor ##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `grade_bsl_1st year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7767 -0.8431 0.2478 1.2318 2.4714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11.0353 7.0436 -1.567 0.1682
## `grade_bsl_1st year` 0.3719 0.1104 3.369 0.0151 *
## self_rating -0.1323 0.7727 -0.171 0.8697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.37 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.6629, Adjusted R-squared: 0.5505
## F-statistic: 5.899 on 2 and 6 DF, p-value: 0.03832# do 2nd-year BSL grades correlate with 3rd year BSL-SRT?
apt_wide %>%
ggplot(aes(x = `grade_bsl_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(50, 71)) +
theme_minimal() + theme_details +
labs(title = "2nd year BSL grade vs. 3rd year BSL-SRT score",
y = "3rd year BSL-SRT score", x = "2nd year BSL grade")
# correlation
apt_wide %>%
filter(`grade_bsl_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`grade_bsl_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.526# linear model
lm(`bsl_srt_3rd year` ~ `grade_bsl_2nd year` +
self_rating, data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `grade_bsl_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 26 28
## -0.4133 0.6953 -3.6954 0.7176 0.7394 1.6953 1.3050 -1.0438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.63326 5.85746 -0.279 0.7915
## `grade_bsl_2nd year` 0.21744 0.09656 2.252 0.0741 .
## self_rating 0.15210 0.66682 0.228 0.8286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.051 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.5312, Adjusted R-squared: 0.3436
## F-statistic: 2.832 on 2 and 5 DF, p-value: 0.1505# marginally insignificantModels
The below combined models are not reported in the manuscript because they are being fit on too few observations
BSL Grades
What best predicts BSL grades?
# wide version - pre-degree
lm(`grade_bsl_2nd year` ~ `nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree` +
#`mlat_acc_pre-degree` +
self_rating +
#`copy_sign_pre-degree`
`grade_bsl_1st year`,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## self_rating + `grade_bsl_1st year`, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8502 -3.1710 0.2518 3.3076 12.9512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -21.6891 37.0515 -0.585 0.571
## `nback_comb_pre-degree` 14.9884 41.4362 0.362 0.725
## `mr3d_sats_pre-degree` -1.4740 1.1047 -1.334 0.212
## `corsi_corr_pre-degree` -0.6435 1.5553 -0.414 0.688
## `kirk_acc_pre-degree` -9.5188 18.9609 -0.502 0.627
## self_rating -0.7772 1.7167 -0.453 0.660
## `grade_bsl_1st year` 1.3569 0.2075 6.540 6.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.248 on 10 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.8255, Adjusted R-squared: 0.7209
## F-statistic: 7.887 on 6 and 10 DF, p-value: 0.002481# final assessments in 3rd year
lm(`grade_bsl_2nd year` ~ `nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year` +
self_rating,
data = apt_wide) %>% summary() ##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 26 28
## 0.9065 -0.3100 0.5909 4.0307 -3.2833 1.4403 -2.0927 -1.2824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -66.525 71.800 -0.927 0.524
## `nback_comb_3rd year` 144.872 138.270 1.048 0.485
## `mr3d_sats_3rd year` 5.334 3.392 1.573 0.361
## `mr2d_sats_3rd year` 3.461 8.506 0.407 0.754
## `corsi_corr_3rd year` 6.253 2.398 2.608 0.233
## `dspan_corr_3rd year` -44.825 101.233 -0.443 0.735
## self_rating -1.854 3.168 -0.585 0.663
##
## Residual standard error: 6.033 on 1 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.9237, Adjusted R-squared: 0.4658
## F-statistic: 2.017 on 6 and 1 DF, p-value: 0.4922BSL-SRT
lm(bsl_srt ~ nback_comb +
mr3d_sats +
corsi_corr +
self_rating,
#kirk_acc +
#kbit_acc +
#dspan_corr,
data = apt) %>% summary()##
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr +
## self_rating, data = apt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.772 -3.277 0.507 2.495 5.864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -46.179130 30.657088 -1.506 0.1663
## nback_comb 75.472442 38.054691 1.983 0.0786 .
## mr3d_sats 1.883576 1.289609 1.461 0.1781
## corsi_corr 0.434593 1.153233 0.377 0.7150
## self_rating 0.007539 1.147214 0.007 0.9949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.226 on 9 degrees of freedom
## (106 observations deleted due to missingness)
## Multiple R-squared: 0.4738, Adjusted R-squared: 0.2399
## F-statistic: 2.026 on 4 and 9 DF, p-value: 0.1742# wide version - pre-degree
lm(`bsl_srt_3rd year` ~
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree` +
self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` +
## `kirk_acc_pre-degree` + self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26 28
## 0.4041 -2.8902 -2.4367 0.5007 1.5638 2.2072 3.0150 -2.9032 0.5392
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.0187 13.9944 -0.430 0.689
## `mr3d_sats_pre-degree` 1.3889 1.1608 1.196 0.298
## `corsi_corr_pre-degree` 1.2925 1.0540 1.226 0.287
## `kirk_acc_pre-degree` 12.9361 11.6096 1.114 0.328
## self_rating -0.5175 1.3071 -0.396 0.712
##
## Residual standard error: 3.156 on 4 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.6017, Adjusted R-squared: 0.2034
## F-statistic: 1.511 on 4 and 4 DF, p-value: 0.3496# final assessments in 3rd year
lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
self_rating,
#`dspan_corr_3rd year`,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## 0.64448 -1.15297 -0.51955 1.11033 0.48620 0.18338 1.54004 0.07878
## 28
## -2.37069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -52.77065 23.15888 -2.279 0.107
## `nback_comb_3rd year` 60.41687 30.88973 1.956 0.145
## `mr3d_sats_3rd year` 0.09283 1.08460 0.086 0.937
## `mr2d_sats_3rd year` 3.64381 1.85882 1.960 0.145
## `corsi_corr_3rd year` 2.40889 0.64130 3.756 0.033 *
## self_rating -0.71772 0.94426 -0.760 0.502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.959 on 3 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.8848, Adjusted R-squared: 0.6929
## F-statistic: 4.61 on 5 and 3 DF, p-value: 0.1192Interpreting Grades
lm(grade_terp ~ nback_spat +
mr3d_sats +
corsi_corr +
kirk_acc +
dspan_corr +
self_rating,
data = apt) %>% summary()##
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + dspan_corr + self_rating, data = apt)
##
## Residuals:
## 66 69 70 73 75 77 78 80 81 86
## 5.0088 0.6792 -4.9458 1.6493 -3.0451 -2.0404 0.5211 -1.7493 4.8945 -0.9723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 341.0421 91.4250 3.730 0.0336 *
## nback_spat -313.8872 99.3076 -3.161 0.0508 .
## mr3d_sats 6.3805 2.1097 3.024 0.0566 .
## corsi_corr -7.3209 2.3355 -3.135 0.0519 .
## kirk_acc 59.7383 20.9992 2.845 0.0654 .
## dspan_corr -52.5709 26.7487 -1.965 0.1441
## self_rating 0.6754 2.2121 0.305 0.7801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.609 on 3 degrees of freedom
## (110 observations deleted due to missingness)
## Multiple R-squared: 0.8868, Adjusted R-squared: 0.6605
## F-statistic: 3.918 on 6 and 3 DF, p-value: 0.1449# wide version - pre-degree
lm(`grade_terp_2nd year` ~ `nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree` +
`dspan_corr_pre-degree`,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `dspan_corr_pre-degree`, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.520 -1.366 1.526 2.695 10.381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.6037 52.7366 -0.068 0.947
## `nback_comb_pre-degree` 40.0142 60.4054 0.662 0.529
## `mr3d_sats_pre-degree` 1.5724 1.5528 1.013 0.345
## `corsi_corr_pre-degree` 1.9683 3.1280 0.629 0.549
## `kirk_acc_pre-degree` -0.5881 74.3808 -0.008 0.994
## `dspan_corr_pre-degree` 30.7403 63.5947 0.483 0.644
##
## Residual standard error: 9.166 on 7 degrees of freedom
## (17 observations deleted due to missingness)
## Multiple R-squared: 0.5111, Adjusted R-squared: 0.1619
## F-statistic: 1.463 on 5 and 7 DF, p-value: 0.3116# final assessments in 3rd year
lm(`grade_terp_2nd year` ~ `nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year` +
self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` +
## `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` +
## `dspan_corr_3rd year` + self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 26 28
## 0.4441 -0.1519 0.2895 1.9748 -1.6086 0.7057 -1.0253 -0.6283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -20.190 35.178 -0.574 0.668
## `nback_comb_3rd year` -39.666 67.744 -0.586 0.663
## `mr3d_sats_3rd year` 3.433 1.662 2.066 0.287
## `mr2d_sats_3rd year` 14.624 4.168 3.509 0.177
## `corsi_corr_3rd year` 4.653 1.175 3.961 0.157
## `dspan_corr_3rd year` 68.163 49.598 1.374 0.400
## self_rating 2.481 1.552 1.599 0.356
##
## Residual standard error: 2.956 on 1 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.9825, Adjusted R-squared: 0.8777
## F-statistic: 9.371 on 6 and 1 DF, p-value: 0.245BSL>Eng interpreting
lm(terp_b2e ~ nback_spat +
mr3d_sats +
corsi_corr +
dspan_corr +
self_rating,
data = apt) %>% summary()##
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr + self_rating, data = apt)
##
## Residuals:
## 103 105 107 108 110 111 112 118
## -3.714 -1.343 1.808 3.084 -3.811 -2.718 5.330 1.364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.065 65.433 0.307 0.788
## nback_spat 60.044 89.747 0.669 0.572
## mr3d_sats 2.279 3.358 0.679 0.567
## corsi_corr 2.615 4.115 0.635 0.590
## dspan_corr -27.812 72.266 -0.385 0.737
## self_rating -1.871 3.927 -0.476 0.681
##
## Residual standard error: 6.346 on 2 degrees of freedom
## (112 observations deleted due to missingness)
## Multiple R-squared: 0.3849, Adjusted R-squared: -1.153
## F-statistic: 0.2503 on 5 and 2 DF, p-value: 0.9081# wide version - pre-degree
lm(`terp_b2e_3rd year` ~ `nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`dspan_corr_pre-degree`+
self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## ALL 5 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 911.589 NA NA NA
## `nback_comb_pre-degree` -464.608 NA NA NA
## `mr3d_sats_pre-degree` 1.394 NA NA NA
## `corsi_corr_pre-degree` 22.437 NA NA NA
## `dspan_corr_pre-degree` -1009.482 NA NA NA
## self_rating NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 4 and 0 DF, p-value: NAlm(`terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` +
self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 28
## -4.4451 0.3171 0.6291 -0.3183 -1.4933 -2.9145 2.7963 5.4287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.4684 3.1264 21.260 4.27e-06 ***
## `mr3d_sats_pre-degree` 2.4042 1.1997 2.004 0.101
## self_rating 0.2235 1.2903 0.173 0.869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.698 on 5 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.4779, Adjusted R-squared: 0.2691
## F-statistic: 2.289 on 2 and 5 DF, p-value: 0.1969# final assessments in 3rd year
lm(`terp_b2e_3rd year` ~ `nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year` +
self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 28
## -1.2305 1.8597 1.3958 0.1906 0.3307 -5.6281 2.6628 0.4190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.2229 80.3244 0.551 0.680
## `nback_comb_3rd year` 33.9926 128.3111 0.265 0.835
## `mr3d_sats_3rd year` -3.1494 6.5776 -0.479 0.716
## `mr2d_sats_3rd year` 8.3609 8.1955 1.020 0.494
## `corsi_corr_3rd year` -0.1087 4.4991 -0.024 0.985
## `dspan_corr_3rd year` -8.7053 80.3181 -0.108 0.931
## self_rating 2.6857 4.9209 0.546 0.682
##
## Residual standard error: 6.783 on 1 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.6487, Adjusted R-squared: -1.459
## F-statistic: 0.3077 on 6 and 1 DF, p-value: 0.8785Eng>BSL interpreting
lm(terp_e2b ~ nback_spat +
mr3d_sats +
corsi_corr +
dspan_corr +
self_rating,
data = apt) %>% summary()##
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr + self_rating, data = apt)
##
## Residuals:
## 103 105 107 108 110 111 112 116
## -17.4064 -1.6379 -0.8478 -0.2062 -14.0278 0.3512 15.1243 7.4936
## 118
## 11.1570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -74.3498 177.8746 -0.418 0.704
## nback_spat 124.0204 243.5084 0.509 0.646
## mr3d_sats 0.4579 6.6724 0.069 0.950
## corsi_corr 4.0471 6.6784 0.606 0.587
## dspan_corr 14.3122 168.1128 0.085 0.938
## self_rating -1.0351 8.2803 -0.125 0.908
##
## Residual standard error: 17.44 on 3 degrees of freedom
## (111 observations deleted due to missingness)
## Multiple R-squared: 0.2709, Adjusted R-squared: -0.9442
## F-statistic: 0.223 on 5 and 3 DF, p-value: 0.9301# wide version - pre-degree
lm(`terp_e2b_3rd year` ~ `nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`dspan_corr_pre-degree` +
self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## ALL 6 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -201.531 NA NA NA
## `nback_comb_pre-degree` 244.387 NA NA NA
## `mr3d_sats_pre-degree` 8.151 NA NA NA
## `corsi_corr_pre-degree` 17.899 NA NA NA
## `dspan_corr_pre-degree` -17.186 NA NA NA
## self_rating -18.894 NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (24 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 5 and 0 DF, p-value: NAlm(`terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` +
self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.243 -5.679 1.404 2.243 18.096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.273 10.114 6.157 0.000842 ***
## `mr3d_sats_pre-degree` 5.158 3.917 1.317 0.235955
## self_rating 2.062 3.973 0.519 0.622317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.09 on 6 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2997, Adjusted R-squared: 0.06625
## F-statistic: 1.284 on 2 and 6 DF, p-value: 0.3435# final assessments in 3rd year
lm(`terp_e2b_3rd year` ~ `nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year` +
self_rating,
data = apt_wide) %>% summary()##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## -1.73737 3.30228 3.00420 2.93128 -1.47311 -9.67350 5.01160 -1.33559
## 28
## -0.02979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -59.757 103.971 -0.575 0.6235
## `nback_comb_3rd year` 143.253 165.297 0.867 0.4775
## `mr3d_sats_3rd year` -8.983 4.862 -1.848 0.2059
## `mr2d_sats_3rd year` 25.403 8.402 3.024 0.0942 .
## `corsi_corr_3rd year` 6.513 3.486 1.868 0.2027
## `dspan_corr_3rd year` -62.423 96.230 -0.649 0.5831
## self_rating 4.204 4.274 0.984 0.4290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.78 on 2 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.8768, Adjusted R-squared: 0.5073
## F-statistic: 2.373 on 6 and 2 DF, p-value: 0.3259This concludes the BSL/SLI predictors analysis.